Natural Bacterial Communities Serve as Quantitative Geochemical Biosensors [electronic resource].

Published:: Washington, D.C. : United States. Dept. of Energy. Office of Science, 2015.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
Physical Description:: pages 1-15 : digital, PDF file
Additional Creators:: Oak Ridge National Laboratory, United States. Department of Energy. Office of Science, and United States. Department of Energy. Office of Scientific and Technical Information

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Summary:

We report that biological sensors can be engineered to measure a wide range of environmental conditions. Here we show that statistical analysis of DNA from natural microbial communities can be used to accurately identify environmental contaminants, including uranium and nitrate at a nuclear waste site. In addition to contamination, sequence data from the 16S rRNA gene alone can quantitatively predict a rich catalogue of 26 geochemical features collected from 93 wells with highly differing geochemistry characteristics. We extend this approach to identify sites contaminated with hydrocarbons from the Deepwater Horizon oil spill, finding that altered bacterial communities encode a memory of prior contamination, even after the contaminants themselves have been fully degraded. We show that the bacterial strains that are most useful for detecting oil and uranium are known to interact with these substrates, indicating that this statistical approach uncovers ecologically meaningful interactions consistent with previous experimental observations. Future efforts should focus on evaluating the geographical generalizability of these associations. Taken as a whole, these results indicate that ubiquitous, natural bacterial communities can be used as in situ environmental sensors that respond to and capture perturbations caused by human impacts. These in situ biosensors rely on environmental selection rather than directed engineering, and so this approach could be rapidly deployed and scaled as sequencing technology continues to become faster, simpler, and less expensive.

Report Numbers:

E 1.99:1265499

Subject(s):

Other Subject(s):

Enigma

Note:

Published through SciTech Connect.
05/12/2015.
"KP1601010"
"ERKP691"
mBio (Online) 6 3 ISSN 2150-7511 AM
Mark B. Smith; Andrea M. Rocha; Chris S. Smillie; Scott W. Olesen; Charles Paradis; Liyou Wu; James H. Campbell; Julian L. Fortney; Tonia L. Mehlhorn; Kenneth A. Lowe; Jennifer E. Earles; Jana Phillips; Steve M. Techtmann; Dominique C. Joyner; Dwayne A. Elias; Kathryn L. Bailey; Richard A. Hurt; Sarah P. Preheim; Matthew C. Sanders; Joy Yang; Marcella A. Mueller; Scott Brooks; David B. Watson; Ping Zhang; Zhili He; et al.

Funding Information:

AC05-00OR22725

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